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This paper aims to investigate recycling behavior (RB) in the United Arab Emirates (UAE) to identify factors in the lack of citizen participation in recycling and to form strategies to raise awareness of and encourage positive recycling practices.

Based on stakeholder theory, a panel of 15 experts and 15 families was interviewed to develop a model of seven constructs and their 29 indicators. Based on the responses generated, a questionnaire was developed and tested. The survey was distributed to 106 waste management professionals. Their responses regarding the influence of regulatory promotion tactics, awareness raising, situational facilitators, motivators and synergistic habits on citizens' RB were analyzed using the structural equation modeling technique.

Several factors govern the citizens' behavior regarding recycling municipal solid waste in the UAE. The most significant governing determinants of RB observed were laws and regulations, willingness to sort and recycle and benefits of recycling. Seven constructs and their 29 indicators were studied, and the findings indicated that strategies such as creating awareness, introducing operative waste control facilities and implementing fines and regulations could improve RB in the UAE. The structural model showcased a relationship between the primary constructs and RB; hence, these constructs can directly affect waste management.

To support these findings, validation of the results from other countries and contrasting employees' RB is needed. The present study draws empirical insights into RB from a stakeholder perspective, which could be compared to RB across other cultures and countries.

This paper aims to report upon the further development of a hybrid application programming interface (API) plug-in to building information modelling (BIM) entitled confined spaces safety monitoring system “CoSMoS”. Originally designed to engineer-out environmental hazards associated with working in a building’s confined spaces (during the construction phase of a building’s life-cycle), this second generation version is expanded upon to use archival records to proactively learn from data generated within a sensor network during the building’s operations and maintenance (O&M) phase of asset management (AM).

An applied research methodological approach adopted used a two-phase process. In phase one, a conceptual model was created to provide a “blueprint map” to integrate BIM, sensor-based networks and data analytics (DA) into one integral system. A literature review provided the basis for the conceptual model’s further development. In phase two, the conceptual model was transposed into the prototype’s development environment as a proof of concept using primary data accrued from a large educational building.

An amalgamation of BIM, historical sensor data accrued and the application of DA demonstrate that CoSMoS provides an opportunity for the facilities management (FM) team to monitor pertinent environmental conditions and human behaviour within buildings that may impact upon occupant/worker safety. Although working in confined spaces is used to demonstrate the inherent potential of CoSMoS, the system could readily be expanded to analyse sensor-based network’s historical data of other areas of building performance, maintenance and safety.

This novel prototype has automated safety applications for FM during the asset lifecycle and maintenance phase of a building’s O&M phase of AM. Future work is proposed in several key areas, namely, develop instantaneous indicators of current safety performance within a building; and develop lead indicators of future safety performance of buildings.

Effective management of highways requires management of diverse data sets including traffic volume data, roadway, and road edge and road-side data. Like all major infrastructure clients, highways administration authorities are under pressure to use such platforms for better management of data that, in addition to creating other opportunities, allows improved life cycle management of asset data and predictive analytics. This paper aims to review such opportunities and the value that can be generated through integrated life cycle data management by leveraging Big Data and building information modelling (BIM).

A literature review is initially performed to systematically gather information to identify and understand BIM as a collaborative platform. Data management applications in other industries are also reviewed. Interviews were conducted and two industry workshops were organised to understand BIM implementation challenges within highways development projects and the role BIM can play in bridging inefficiencies resulting from loss of information at handover phases. The overall understanding lead to drawing up user needs, gathering system requirements and eventually a system architecture design to promote efficient information management throughout the asset lifecycle.

It is observed that data from the design and construction phases of projects can be used to inform asset registers from an earlier stage. This information can be used to plan maintenance schedules. Moreover, it can also be integrated with data generated from numerous other sensors to develop a better picture of network operations and support key decision-making. Effective road network management involves collection and analysis of huge data from a variety of sources including sensors, mobiles, assets and Open Data. Recent growth in Big Data analytics and data integration technologies provides new opportunities to optimise operations of highways infrastructure.

The system architecture designed for this research is translated into a prototype system as a proof of concept. However, it needs to be tested and validated by end users to be transformed into a useful solution for the industry.

This paper provides an enhanced understanding of new opportunities created to optimise operations of highways infrastructure using the recent growth in Big Data analytics and data integration technologies.

The purpose of this paper is to explore the current challenges and drivers for data mining in the AEC sector.

Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics.

The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals.

With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and Big Data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for data mining in the AEC sector with a practical focus.

This research aims to investigate the integration of real-time monitoring of thermal conditions within confined work environments through wireless sensor network (WSN) technology when integrated with building information modelling (BIM). A prototype system entitled confined space monitoring system (CoSMoS), which provides an opportunity to incorporate sensor data for improved visualization through new add-ins to BIM software, was then developed.

An empirical study was undertaken to compare and contrast between the performances (over a time series) of various database models to find a back-end database storage configuration that best suits the needs of CoSMoS.

Fusing BIM data with information streams derived from wireless sensors challenges traditional approaches to data management. These challenges encountered in the prototype system are reported upon and include issues such as hardware/software selection and optimization. Consequently, various database models are explored and tested to find a database storage that best suits the specific needs of this BIM-wireless sensor technology integration.

This work represents the first tranche of research that seeks to deliver a fully integrated and advanced digital built environment solution for automating the management of health and safety issues on construction sites.

Construction plant and equipment accident statistics suggest constant re‐evaluation of health and safety (H&S) systems is beneficial. This paper aims to process analyse plant and equipment H&S management systems on UK construction sites, with a view to applying information and communication technology (ICT) to them as an improvement mechanism.

Five construction project case studies drawn from members of the former Major Contractors Group yield rich H&S process data. These are analysed using data flow diagram (DFD) techniques, to evaluate processes and proffer system improvements incorporating ICT.

Causes of unsafe practice regarding management of construction plant and equipment are found to include: aspects of the plant itself, management processes and operator competence. A new ICT “process paradigm” is suggested, the architecture of which incorporates mobile computing, automatic identification and data collection and a management information system.

Findings contribute particularly to the fields of plant and equipment; and managing H&S.

Suggested ICT direction might form the basis of commercial interest in developing an all‐embracing H&S control mechanism for plant and equipment operations.

Application of DFD analysis in this setting is quite new.

Construction is a risky industry. Therefore, organizations are seeking ways towards improving their safety performance. Among these, the integration of technology into health and safety leads to enhanced safety performance. Considering the benefits observed in using technology in safety, this study aims to explore digital technologies' use and potential benefits in construction health and safety.

An extensive bibliometrics analysis was conducted to reveal which technologies are at the forefront of others and how these technologies are used in safety operations. The study used two different databases, Web of Science (WoS) and Scopus, to scan the literature in a systemic way.

The systemic analysis of several studies showed that the digital technologies use in construction are still a niche theme and need more assessment. The study provided that sensors and wireless technology are of utmost importance in terms of construction safety. Moreover, the study revealed that artificial intelligence, machine learning, building information modeling (BIM), sensors and wireless technologies are trending technologies compared to unmanned aerial vehicles, serious games and the Internet of things. On the other hand, the study provided that the technologies are even more effective with integrated use like in the case of BIM and sensors or unmanned aerial vehicles. It was observed that the use of these technologies varies with respect to studies conducted in different countries. The study further revealed that the studies conducted on this topic are mostly published in some selected journals and international collaboration efforts in terms of researching the topic have been observed.

This study provides an extensive analysis of WoS and Scopus databases and an in-depth review of the use of digital technologies in construction safety. The review consists of the most recent studies showing the benefits of using such technologies and showing the usage on a systemic level from which both scientists and practitioners can benefit to devise new strategies in technology usage.

In artificial intelligence, the optical character recognition (OCR) is an active research area based on famous applications such as automation and transformation of printed documents into machine-readable text document. The major purpose of OCR in academia and banks is to achieve a significant performance to save storage space.

A novel technique is proposed for automated OCR based on multi-properties features fusion and selection. The features are fused using serially formulation and output passed to partial least square (PLS) based selection method. The selection is done based on the entropy fitness function. The final features are classified by an ensemble classifier.

The presented method was extensively tested on two datasets such as the authors proposed and Chars74k benchmark and achieved an accuracy of 91.2 and 99.9%. Comparing the results with existing techniques, it is found that the proposed method gives improved performance.

The technique presented in this work will help for license plate recognition and text conversion from a printed document to machine-readable.